1
|
Chen J, Tang H, Wang M, Wei H, Ou C. Explorative study for the rapid detection of adulterated surimi using gas chromatography-ion mobility spectrometry. Food Chem 2024; 439:138083. [PMID: 38043278 DOI: 10.1016/j.foodchem.2023.138083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 11/12/2023] [Accepted: 11/24/2023] [Indexed: 12/05/2023]
Abstract
Driven by economic interests, surimi adulteration has become a high-frequency issue. This study aims to assess the feasibility of gas chromatography-ion mobility spectrometry (GC-IMS) in detecting surimi adulteration. In this work, three common adulterated surimi models were established by mixing with different fish species and ratios. The fingerprints enabled a clear discrimination among different tuna surimi, and other two surimi models with different mixing ratios also showed VOCs (volatile organic compounds) differences. Results of unsupervised principal component analysis (PCA) and supervised partial least-squares discrimination analysis (PLS-DA) revealed that different types of adulterated surimi models can be well separated from each other. A total of 12, 16, and 9 VOCs were selected as the potential markers in three simulated models by PLS-DA method, respectively. Therefore, GC-IMS coupled with certain chemometrics is expected to serve as an alternative analytical tool to directly and visually detect adulterated surimi.
Collapse
Affiliation(s)
- Jingyi Chen
- College of Food and Pharmaceutical Sciences, Ningbo University, Ningbo, Zhejiang 315832, China
| | - Haiqing Tang
- Faculty of Food Science, Zhejiang Pharmaceutical University, Ningbo, Zhejiang 315100, China
| | - Mengyun Wang
- College of Food and Pharmaceutical Sciences, Ningbo University, Ningbo, Zhejiang 315832, China
| | - Huamao Wei
- College of Food and Pharmaceutical Sciences, Ningbo University, Ningbo, Zhejiang 315832, China
| | - Changrong Ou
- College of Food and Pharmaceutical Sciences, Ningbo University, Ningbo, Zhejiang 315832, China; Key Laboratory of Animal Protein Food Deep Processing Technology of Zhejiang Province, Ningbo University, Ningbo, Zhejiang 315832, China.
| |
Collapse
|
2
|
Tang T, Luo Q, Yang L, Gao C, Ling C, Wu W. Research Review on Quality Detection of Fresh Tea Leaves Based on Spectral Technology. Foods 2023; 13:25. [PMID: 38201054 PMCID: PMC10778318 DOI: 10.3390/foods13010025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 12/13/2023] [Accepted: 12/18/2023] [Indexed: 01/12/2024] Open
Abstract
As the raw material for tea making, the quality of tea leaves directly affects the quality of finished tea. The quality of fresh tea leaves is mainly assessed by manual judgment or physical and chemical testing of the content of internal components. Physical and chemical methods are more mature, and the test results are more accurate and objective, but traditional chemical methods for measuring the biochemical indexes of tea leaves are time-consuming, labor-costly, complicated, and destructive. With the rapid development of imaging and spectroscopic technology, spectroscopic technology as an emerging technology has been widely used in rapid non-destructive testing of the quality and safety of agricultural products. Due to the existence of spectral information with a low signal-to-noise ratio, high information redundancy, and strong autocorrelation, scholars have conducted a series of studies on spectral data preprocessing. The correlation between spectral data and target data is improved by smoothing noise reduction, correction, extraction of feature bands, and so on, to construct a stable, highly accurate estimation or discrimination model with strong generalization ability. There have been more research papers published on spectroscopic techniques to detect the quality of tea fresh leaves. This study summarizes the principles, analytical methods, and applications of Hyperspectral imaging (HSI) in the nondestructive testing of the quality and safety of fresh tea leaves for the purpose of tracking the latest research advances at home and abroad. At the same time, the principles and applications of other spectroscopic techniques including Near-infrared spectroscopy (NIRS), Mid-infrared spectroscopy (MIRS), Raman spectroscopy (RS), and other spectroscopic techniques for non-destructive testing of quality and safety of fresh tea leaves are also briefly introduced. Finally, in terms of technical obstacles and practical applications, the challenges and development trends of spectral analysis technology in the nondestructive assessment of tea leaf quality are examined.
Collapse
Affiliation(s)
- Ting Tang
- College of Engineering, South China Agricultural University, Guangzhou 510642, China; (T.T.); (Q.L.); (L.Y.); (C.G.)
| | - Qing Luo
- College of Engineering, South China Agricultural University, Guangzhou 510642, China; (T.T.); (Q.L.); (L.Y.); (C.G.)
| | - Liu Yang
- College of Engineering, South China Agricultural University, Guangzhou 510642, China; (T.T.); (Q.L.); (L.Y.); (C.G.)
| | - Changlun Gao
- College of Engineering, South China Agricultural University, Guangzhou 510642, China; (T.T.); (Q.L.); (L.Y.); (C.G.)
| | - Caijin Ling
- Tea Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China
| | - Weibin Wu
- College of Engineering, South China Agricultural University, Guangzhou 510642, China; (T.T.); (Q.L.); (L.Y.); (C.G.)
| |
Collapse
|
3
|
Yu Q, Huang C, Zhu R, Lu D, Liu L, Lai J, Zhong X, Guan J, Zhou S, Tong Y, Wang Z, Chen P, Guo H, Chu Q, Gong S, Fan F. Chemometrics-based investigation of non-volatiles/volatiles flavor of tencha (Camellia sinensis cv. Yabukita, Longjing 43 and Baiye 1). Food Res Int 2023; 173:113461. [PMID: 37803791 DOI: 10.1016/j.foodres.2023.113461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Revised: 08/30/2023] [Accepted: 09/10/2023] [Indexed: 10/08/2023]
Abstract
The increasing demand for tea consumption calls for the development of more products with distinct characteristics. The sensory quality of tencha is significantly determined by innate differences among tea cultivars. However, the correlations between the chemical composition and sensory traits of tencha are still unclear. To enhance the understanding of the flavor formation mechanism in tencha and further to develop new cultivars resources, we investigated non-volatiles and volatile metabolites as well as sensory traits in tencha from different tea cultivars (Camellia sinensis cv. Yabukita, Longjing 43 and Baiye 1); the relationships between the flavor traits and non-volatiles/volatiles were further evaluated by partial least squares - discriminate analysis (PLS-DA), multiple factor analysis (MFA) and multidimensional alignment (MDA) analysis. A total of 64 non-volatiles and 116 volatiles were detected in all samples, among which 71 metabolites were identified as key flavor-chemical contributors involving amino acids, flavonol glycosides, flavones, catechins, ketones, alcohols, hydrocarbons, aldehydes, esters and acids. The levels of taste-related amino acids, flavonol glycosides and gallic acid varied significantly among the tencha samples made from different tea cultivars. All the samples exhibited typical quality characteristics of tencha. The tencha from Camellia sinensis cv. Longjing 43 and Camellia sinensis cv. Baiye 1 (cultivated in the open) exhibited higher levels of amino acids and gallic acid, which were associated with the umami taste and mellow taste of tea infusion. Abundant flavonol glycosides were related to the astringency, while partial tri-glycosides specifically quercetin-3-O-galactoside-rhamnoside-glucoside and total of flavonol galactoside-rhamnoside-glucoside were associated with mellow taste. The floral alcohols were identified as significant contributors to the refreshing aroma traits of tencha. The green, almond-like, acidic and fruity odorants were associated with a green and fresh aroma, while the green, cheesy and waxy odorants such as ketones, esters, acids and hydrocarbons were associated with seaweed-like aroma. This study provides insight into sensory-related chemical profiles of tencha from different tea cultivars, supplying valuable information on flavor and quality identification for tencha.
Collapse
Affiliation(s)
- Qiuwen Yu
- Tea Research Institute, College of Agriculture and Biotechnolgy, Zhejiang University, Hangzhou 310058, P.R. China
| | - Chuangsheng Huang
- Tea Research Institute, College of Agriculture and Biotechnolgy, Zhejiang University, Hangzhou 310058, P.R. China
| | - Ruolan Zhu
- Tea Research Institute, College of Agriculture and Biotechnolgy, Zhejiang University, Hangzhou 310058, P.R. China
| | - Debiao Lu
- Zhejiang Agricultural Technical Extension Center, Hangzhou 310020, P.R. China
| | - Liping Liu
- Huzhou Agricultural and Rural Bureau, Huzhou 313000, P.R. China
| | - Jianhong Lai
- Anji Agricultural and Rural Bureau, Anji 313300, P.R. China
| | - Xinyao Zhong
- Changxing Agricultural and Rural Bureau, Changxing 313100, P.R. China
| | - Jueshan Guan
- Zhejiang Teaworld Food Co., Ltd, Changxing 313113, P.R. China
| | - Senjie Zhou
- Tea Research Institute, College of Agriculture and Biotechnolgy, Zhejiang University, Hangzhou 310058, P.R. China
| | - Yilin Tong
- Tea Research Institute, College of Agriculture and Biotechnolgy, Zhejiang University, Hangzhou 310058, P.R. China
| | - Zipei Wang
- Tea Research Institute, College of Agriculture and Biotechnolgy, Zhejiang University, Hangzhou 310058, P.R. China
| | - Ping Chen
- Tea Research Institute, College of Agriculture and Biotechnolgy, Zhejiang University, Hangzhou 310058, P.R. China
| | - Haowei Guo
- Tea Research Institute, College of Agriculture and Biotechnolgy, Zhejiang University, Hangzhou 310058, P.R. China
| | - Qiang Chu
- Tea Research Institute, College of Agriculture and Biotechnolgy, Zhejiang University, Hangzhou 310058, P.R. China
| | - Shuying Gong
- Tea Research Institute, College of Agriculture and Biotechnolgy, Zhejiang University, Hangzhou 310058, P.R. China.
| | - Fangyuan Fan
- Tea Research Institute, College of Agriculture and Biotechnolgy, Zhejiang University, Hangzhou 310058, P.R. China.
| |
Collapse
|
4
|
Zhang Y, Wang Y. Machine learning applications for multi-source data of edible crops: A review of current trends and future prospects. Food Chem X 2023; 19:100860. [PMID: 37780348 PMCID: PMC10534232 DOI: 10.1016/j.fochx.2023.100860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 08/23/2023] [Accepted: 08/31/2023] [Indexed: 10/03/2023] Open
Abstract
The quality and safety of edible crops are key links inseparable from human health and nutrition. In the era of rapid development of artificial intelligence, using it to mine multi-source information on edible crops provides new opportunities for industrial development and market supervision of edible crops. This review comprehensively summarized the applications of multi-source data combined with machine learning in the quality evaluation of edible crops. Multi-source data can provide more comprehensive and rich information from a single data source, as it can integrate different data information. Supervised and unsupervised machine learning is applied to data analysis to achieve different requirements for the quality evaluation of edible crops. Emphasized the advantages and disadvantages of techniques and analysis methods, the problems that need to be overcome, and promising development directions were proposed. To monitor the market in real-time, the quality evaluation methods of edible crops must be innovated.
Collapse
Affiliation(s)
- Yanying Zhang
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming 650200, China
- College of Traditional Chinese Medicine, Yunnan University of Chinese Medicine, Kunming 650500, China
| | - Yuanzhong Wang
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming 650200, China
| |
Collapse
|
5
|
Chu X, Zhang K, Wei H, Ma Z, Fu H, Miao P, Jiang H, Liu H. A Vis/NIR spectra-based approach for identifying bananas infected with Colletotrichum musae. FRONTIERS IN PLANT SCIENCE 2023; 14:1180203. [PMID: 37332705 PMCID: PMC10272841 DOI: 10.3389/fpls.2023.1180203] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Accepted: 05/09/2023] [Indexed: 06/20/2023]
Abstract
Introduction Anthracnose of banana caused by Colletotrichum species is one of the most serious post-harvest diseases, which can cause significant yield losses. Clarifying the infection mechanism of the fungi using non-destructive methods is crucial for timely discriminating infected bananas and taking preventive and control measures. Methods This study presented an approach for tracking growth and identifying different infection stages of the C. musae in bananas using Vis/NIR spectroscopy. A total of 330 banana reflectance spectra were collected over ten consecutive days after inoculation, with a sampling rate of 24 h. The four-class and five-class discriminant patterns were designed to examine the capability of NIR spectra in discriminating bananas infected at different levels (control, acceptable, moldy, and highly moldy), and different time at early stage (control and days 1-4). Three traditional feature extraction methods, i.e. PC loading coefficient (PCA), competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA), combining with two machine learning methods, i.e. partial least squares discriminant analysis (PLSDA) and support vector machine (SVM), were employed to build discriminant models. One-dimensional convolutional neural network (1D-CNN) without manually extracted feature parameters was also introduced for comparison. Results The PCA-SVM and·SPA-SVM models had good performance with identification accuracies of 93.98% and 91.57%, 94.47% and 89.47% in validation sets for the four- and five-class patterns, respectively. While the 1D-CNN models performed the best, achieving an accuracy of 95.18% and 97.37% for identifying infected bananas at different levels and time, respectively. Discussion These results indicate the feasibility of identifying banana fruit infected with C. musae using Vis/NIR spectra, and the resolution can be accurate to one day.
Collapse
Affiliation(s)
- Xuan Chu
- College of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou, China
| | - Kun Zhang
- College of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou, China
| | - Hongyu Wei
- College of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou, China
| | - Zhiyu Ma
- College of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou, China
| | - Han Fu
- College of Engineering, South China Agricultural University, Guangzhou, China
| | - Pu Miao
- College of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou, China
| | - Hongzhe Jiang
- College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing, China
| | - Hongli Liu
- College of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou, China
| |
Collapse
|
6
|
Zhou S, Zhang J, Ma S, Ou C, Feng X, Pan Y, Gong S, Fan F, Chen P, Chu Q. Recent advances on white tea: Manufacturing, compositions, aging characteristics and bioactivities. Trends Food Sci Technol 2023. [DOI: 10.1016/j.tifs.2023.02.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/27/2023]
|
7
|
Zhang L, Dai H, Zhang J, Zheng Z, Song B, Chen J, Lin G, Chen L, Sun W, Huang Y. A Study on Origin Traceability of White Tea (White Peony) Based on Near-Infrared Spectroscopy and Machine Learning Algorithms. Foods 2023; 12:foods12030499. [PMID: 36766027 PMCID: PMC9914092 DOI: 10.3390/foods12030499] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 01/15/2023] [Accepted: 01/18/2023] [Indexed: 01/24/2023] Open
Abstract
Identifying the geographical origins of white tea is of significance because the quality and price of white tea from different production areas vary largely from different growing environment and climatic conditions. In this study, we used near-infrared spectroscopy (NIRS) with white tea (n = 579) to produce models to discriminate these origins under different conditions. Continuous wavelet transform (CWT), min-max normalization (Minmax), multiplicative scattering correction (MSC) and standard normal variables (SNV) were used to preprocess the original spectra (OS). The approaches of principal component analysis (PCA), linear discriminant analysis (LDA) and successive projection algorithm (SPA) were used for features extraction. Subsequently, identification models of white tea from different provinces of China (DPC), different districts of Fujian Province (DDFP) and authenticity of Fuding white tea (AFWT) were established by K-nearest neighbors (KNN), random forest (RF) and support vector machine (SVM) algorithms. Among the established models, DPC-CWT-LDA-KNN, DDFP-OS-LDA-KNN and AFWT-OS-LDA-KNN have the best performances, with recognition accuracies of 88.97%, 93.88% and 97.96%, respectively; the area under curve (AUC) values were 0.85, 0.93 and 0.98, respectively. The research revealed that NIRS with machine learning algorithms can be an effective tool for the geographical origin traceability of white tea.
Collapse
Affiliation(s)
- Lingzhi Zhang
- College of Horticulture, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Haomin Dai
- College of Horticulture, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Jialin Zhang
- College of Horticulture, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Zhiqiang Zheng
- College of Horticulture, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Bo Song
- College of Horticulture, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Jiaya Chen
- LiuMiao White Tea Corporation, Fuding 355200, China
| | - Gang Lin
- Fujian Rongyuntong Ecological Technology Limited Company, Fuzhou 350025, China
| | - Linhai Chen
- Fu’an Tea Industry Development Center, Fu’an 355000, China
| | - Weijiang Sun
- College of Horticulture, Fujian Agriculture and Forestry University, Fuzhou 350002, China
- Institute of China White Tea, Fuding 355200, China
- Correspondence: (W.S.); (Y.H.)
| | - Yan Huang
- College of Horticulture, Fujian Agriculture and Forestry University, Fuzhou 350002, China
- Institute of China White Tea, Fuding 355200, China
- Anxi College of Tea Science, Fujian Agriculture and Forestry University, Quanzhou 362400, China
- Correspondence: (W.S.); (Y.H.)
| |
Collapse
|
8
|
Jin S, Sun F, Hu Z, Li Y, Zhao Z, Du G, Shi G, Chen J. Online quantitative substrate, product, and cell concentration in citric acid fermentation using near-infrared spectroscopy combined with chemometrics. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 285:121842. [PMID: 36126619 DOI: 10.1016/j.saa.2022.121842] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 08/08/2022] [Accepted: 09/02/2022] [Indexed: 06/15/2023]
Abstract
As a mature platform compound, citric acid (CA) is mainly produced by Aspergillus niger (A. niger) through submerged fermentation. However, the CA fermentation process is still regulated based on experience and limited offline data, so real-time monitoring and intelligent precise control of the fermentation process cannot be carried out. In this study, near-infrared (NIR) spectroscopy combined with different chemometrics methods was used to quantify the substrate, product, and cell concentration of CA fermentation online. The predictive performance of total sugar (TS), CA, and dry cell weight (DCW) concentrations were compared between traditional partial least squares (PLS) and intelligent stacked auto-encoder (SAE) modeling methods. Theresults showed that both PLS and SAE models had good performance in predicting TS and CA. The performance, accuracy, and precision of the PLS models are slightly better than those of the SAE models in predicting TS and CA. SAE model was superior to the PLS model in predicting DCW concentration. The SAE modeling method has advantages in predicting the concentration of complex components. In this study, the multi-parameter online prediction was realized in the complex system of CA fermentation, which provided the basis for real-time intelligent control of the fermentation process.
Collapse
Affiliation(s)
- Sai Jin
- National Engineering Research Center of Cereal Fermentation and Food Biomanufacturing, Jiangnan University, Wuxi, Jiangsu Province 214122, People's Republic of China; Jiangsu Guoxin Union Energy Co., Ltd., Wuxi, Jiangsu Province 214203, People's Republic of China
| | - Fuxin Sun
- Jiangsu Guoxin Union Energy Co., Ltd., Wuxi, Jiangsu Province 214203, People's Republic of China
| | - Zhijie Hu
- Jiangsu Guoxin Union Energy Co., Ltd., Wuxi, Jiangsu Province 214203, People's Republic of China
| | - Youran Li
- National Engineering Research Center of Cereal Fermentation and Food Biomanufacturing, Jiangnan University, Wuxi, Jiangsu Province 214122, People's Republic of China
| | - Zhonggai Zhao
- Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi, Jiangsu Province 214122, People's Republic of China
| | - Guocheng Du
- Science Center for Future Foods, Jiangnan University, Wuxi, Jiangsu Province 214122, People's Republic of China
| | - Guiyang Shi
- National Engineering Research Center of Cereal Fermentation and Food Biomanufacturing, Jiangnan University, Wuxi, Jiangsu Province 214122, People's Republic of China.
| | - Jian Chen
- National Engineering Research Center of Cereal Fermentation and Food Biomanufacturing, Jiangnan University, Wuxi, Jiangsu Province 214122, People's Republic of China; Science Center for Future Foods, Jiangnan University, Wuxi, Jiangsu Province 214122, People's Republic of China.
| |
Collapse
|
9
|
Liu S, Wang S, Hu C, Zhan S, Kong D, Wang J. Rapid and accurate determination of diesel multiple properties through NIR data analysis assisted by machine learning. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 277:121261. [PMID: 35490664 DOI: 10.1016/j.saa.2022.121261] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Revised: 04/05/2022] [Accepted: 04/10/2022] [Indexed: 06/14/2023]
Abstract
The rapid and accurate detection of diesel multiple properties is an important research topic in petrochemical industry that is conducive to diesel quality assessment and environmental pollution mitigation. To that end, this paper developed a new machine learning model for near infrared (NIR) spectroscopy capable of simultaneously determining diesel density, viscosity, freezing point, boiling point, cetane number and total aromatics. The model combined improved XY co-occurrence distance (ISPXY) and differential evolution-gray wolf optimization support vector machine (DEGWO-SVM) to attain the goal of rapidity and accuracy. Experimental results indicated that the average recovery, mean square error, mean absolute percentage error and determination coefficient of the presented method outperformed those of the existing machine learning methods. The proposed hybrid model provides superior solution to the problem of low efficiency and high cost of diesel quality detection, and has the potential to be utilized as a promising tool for diesel routine monitoring.
Collapse
Affiliation(s)
- Shiyu Liu
- Measurement Technology and Instrumentation Key Lab of Hebei Province, School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China
| | - Shutao Wang
- Measurement Technology and Instrumentation Key Lab of Hebei Province, School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China.
| | - Chunhai Hu
- Measurement Technology and Instrumentation Key Lab of Hebei Province, School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China
| | - Shujie Zhan
- Measurement Technology and Instrumentation Key Lab of Hebei Province, School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China
| | - Deming Kong
- Measurement Technology and Instrumentation Key Lab of Hebei Province, School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China
| | - Junzhu Wang
- Flow Measurement Technology Key Lab of Zhejiang Province, College of Metrology & Measurement Engineering, China Jiliang University, Hangzhou, Zhejiang 310018, China
| |
Collapse
|
10
|
Xie H, Chen Z, Feng X, Wang M, Luo Y, Wang Y, Xu P. L-theanine exuded from Camellia sinensis roots regulates element cycling in soil by shaping the rhizosphere microbiome assembly. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 837:155801. [PMID: 35561922 DOI: 10.1016/j.scitotenv.2022.155801] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 05/04/2022] [Accepted: 05/05/2022] [Indexed: 06/15/2023]
Abstract
Root exudate metabolites are a key medium for the interaction between plants and soil microbiota. L-theanine is a unique non-protein amino acid critical for the flavor and potential health benefits of tea products; however, its biological function in tea plants is not well understood. As L-theanine is mainly synthesized in the roots of tea plants, we hypothesized that L-theanine could affect the function of the rhizosphere microbiota by modulating microbial assembly. In the present study, L-theanine was detected in the exudates of tea plant roots using liquid chromatography-mass spectrometry. Additionally, 16S rRNA gene sequencing revealed that L-theanine significantly altered the structure of the rhizosphere microbiota and selectively shaped rhizosphere microbial assembly. Moreover, metagenomic data showed that L-theanine affected the abundance of genes encoding element cycling in soil. Interestingly, the denitrification and complete nitrification pathways were significantly inhibited by L-theanine by decreasing the narH, napA, and napB genes abundance. These findings provide new insights into the biological function of L-theanine, as well as the implications of interactions between tea plant root exudates and the rhizosphere microbiome.
Collapse
Affiliation(s)
- Hengtong Xie
- Institute of Tea Science, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Horticultural Plant Growth, Development and Quality Improvement, Ministry of Agriculture, Hangzhou 310058, China
| | - Zimeng Chen
- Institute of Tea Science, Zhejiang University, Hangzhou 310058, China
| | - Xiaoxiao Feng
- Agricultural Experiment Station, Zhejiang University, Hangzhou 310058, China
| | - Mengcen Wang
- Key Laboratory of Molecular Biology of Crop Pathogens and Insects, Ministry of Agriculture, Zhejiang University, Hangzhou 310058, China
| | - Yu Luo
- Institute of Soil & Water Resources and Environmental Science, Zhejiang University, Hangzhou 310058, China
| | - Yuefei Wang
- Institute of Tea Science, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Horticultural Plant Growth, Development and Quality Improvement, Ministry of Agriculture, Hangzhou 310058, China
| | - Ping Xu
- Institute of Tea Science, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Horticultural Plant Growth, Development and Quality Improvement, Ministry of Agriculture, Hangzhou 310058, China.
| |
Collapse
|
11
|
Ding Y, Yan Y, Li J, Chen X, Jiang H. Classification of Tea Quality Levels Using Near-Infrared Spectroscopy Based on CLPSO-SVM. Foods 2022; 11:foods11111658. [PMID: 35681408 PMCID: PMC9180160 DOI: 10.3390/foods11111658] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 05/27/2022] [Accepted: 05/31/2022] [Indexed: 02/04/2023] Open
Abstract
In this paper, we propose a method for classifying tea quality levels based on near-infrared spectroscopy. Firstly, the absorbance spectra of Huangshan Maofeng tea samples were obtained in a wavenumber range of 10,000~4000 cm−1 using near-infrared spectroscopy. The spectral data were then converted to transmittance and smoothed using the Savitzky–Golay (SG) algorithm. The denoised transmittance spectra were dimensionally reduced using principal component analysis (PCA). The characteristic variables obtained using PCA were used as the input variables and the tea level was used as the output to establish a support vector machine (SVM) classification model. The penalty factor c and the kernel function parameter g in the SVM model were optimized using particle swarm optimization (PSO) and comprehensive-learning particle swarm optimization (CLPSO) algorithms. The final experimental results show that the CLPSO-SVM method had the best classification performance, and the classification accuracy reached 99.17%.
Collapse
Affiliation(s)
- Yuhan Ding
- Key Laboratory of Modern Agricultural Equipment and Technology, Jiangsu University, Ministry of Education, Zhenjiang 212013, China; (Y.D.); (J.L.)
- High-Tech Key Laboratory of Agricultural Equipment and Intelligence of Jiangsu Province, Jiangsu University, Zhenjiang 212013, China
- Institute of High-Performance Electrical Machine System and Intelligent Control, Jiangsu University, Zhenjiang 212013, China
| | - Yuli Yan
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China; (Y.Y.); (X.C.)
| | - Jun Li
- Key Laboratory of Modern Agricultural Equipment and Technology, Jiangsu University, Ministry of Education, Zhenjiang 212013, China; (Y.D.); (J.L.)
- High-Tech Key Laboratory of Agricultural Equipment and Intelligence of Jiangsu Province, Jiangsu University, Zhenjiang 212013, China
- School of Automation, Southeast University, Nanjing 210096, China
| | - Xu Chen
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China; (Y.Y.); (X.C.)
| | - Hui Jiang
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China; (Y.Y.); (X.C.)
- Correspondence:
| |
Collapse
|
12
|
Fan FY, Zhou SJ, Qian H, Zong BZ, Huang CS, Zhu RL, Guo HW, Gong SY. Effect of Yellowing Duration on the Chemical Profile of Yellow Tea and the Associations with Sensory Traits. Molecules 2022; 27:molecules27030940. [PMID: 35164205 PMCID: PMC8839223 DOI: 10.3390/molecules27030940] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 01/18/2022] [Accepted: 01/26/2022] [Indexed: 02/04/2023] Open
Abstract
The yellowing process is the crucial step to form the characteristic sensory and chemical properties of yellow tea. To investigate the chemical changes and the associations with sensory traits during yellowing, yellow teas with different yellowing times (0–13 h) were prepared for sensory evaluation and chemical analysis. The intensities of umami and green-tea aroma were reduced whereas sweet taste, mellow taste and sweet aroma were increased under long-term yellowing treatment. A total of 230 chemical constituents were determined, among which 25 non-volatiles and 42 volatiles were the key chemical contributors to sensory traits based on orthogonal partial least squares discrimination analysis (OPLS-DA), multiple factor analysis (MFA) and multidimensional alignment (MDA) analysis. The decrease in catechins, flavonol glycosides and caffeine and the increase in certain amino acids contributed to the elevated sweet taste and mellow taste. The sweet, woody and herbal odorants and the fermented and fatty odorants were the key contributors to the characteristic sensory feature of yellow tea with sweet aroma and over-oxidation aroma, including 7 ketones, 5 alcohols, 1 aldehyde, 5 acids, 4 esters, 5 hydrocarbons, 1 phenolic compound and 1 sulfocompound. This study reveals the sensory trait-related chemical changes in the yellowing process of tea, which provides a theoretical basis for the optimization of the yellowing process and quality control of yellow tea.
Collapse
Affiliation(s)
- Fang-Yuan Fan
- Tea Research Institute, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China; (F.-Y.F.); (S.-J.Z.); (B.-Z.Z.); (C.-S.H.); (R.-L.Z.); (H.-W.G.)
| | - Sen-Jie Zhou
- Tea Research Institute, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China; (F.-Y.F.); (S.-J.Z.); (B.-Z.Z.); (C.-S.H.); (R.-L.Z.); (H.-W.G.)
| | - Hong Qian
- Deqing Agricultural Technology Extension Center, 883 Zhongxingbei Road, Huzhou 313200, China;
| | - Bang-Zheng Zong
- Tea Research Institute, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China; (F.-Y.F.); (S.-J.Z.); (B.-Z.Z.); (C.-S.H.); (R.-L.Z.); (H.-W.G.)
| | - Chuang-Sheng Huang
- Tea Research Institute, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China; (F.-Y.F.); (S.-J.Z.); (B.-Z.Z.); (C.-S.H.); (R.-L.Z.); (H.-W.G.)
| | - Ruo-Lan Zhu
- Tea Research Institute, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China; (F.-Y.F.); (S.-J.Z.); (B.-Z.Z.); (C.-S.H.); (R.-L.Z.); (H.-W.G.)
| | - Hao-Wei Guo
- Tea Research Institute, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China; (F.-Y.F.); (S.-J.Z.); (B.-Z.Z.); (C.-S.H.); (R.-L.Z.); (H.-W.G.)
| | - Shu-Ying Gong
- Tea Research Institute, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China; (F.-Y.F.); (S.-J.Z.); (B.-Z.Z.); (C.-S.H.); (R.-L.Z.); (H.-W.G.)
- Correspondence: ; Tel.: +86-(571)-88982519
| |
Collapse
|
13
|
Development of analytical method associating near-infrared spectroscopy with one-dimensional convolution neural network: a case study. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2021. [DOI: 10.1007/s11694-021-00878-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
|
14
|
Ong P, Chen S, Tsai CY, Chuang YK. Prediction of tea theanine content using near-infrared spectroscopy and flower pollination algorithm. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 255:119657. [PMID: 33744842 DOI: 10.1016/j.saa.2021.119657] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Revised: 02/16/2021] [Accepted: 02/28/2021] [Indexed: 06/12/2023]
Abstract
In this study, near-infrared (NIR) spectroscopy was exploited for non-destructive determination of theanine content of oolong tea. The NIR spectral data (400-2500 nm) were correlated with the theanine level of 161 tea samples using partial least squares regression (PLSR) with different wavelengths selection methods, including the regression coefficient-based selection, uninformative variable elimination, variable importance in projection, selectivity ratio and flower pollination algorithm (FPA). The potential of using the FPA to select the discriminative wavelengths for PLSR was examined for the first time. The analysis showed that the PLSR with FPA method achieved better predictive results than the PLSR with full spectrum (PLSR-full). The developed simplified model using on FPA based on 12 latent variables and 89 selected wavelengths produced R-squared (R2) value and root mean squared error (RMSE) of 0.9542, 0.8794 and 0.2045, 0.3219 for calibration and prediction, respectively. For PLSR-full, the R2 values of 0.9068, 0.8412 and RMSEs of 0.2916, 0.3693, were achieved for calibration and prediction. Also, the optimized model using FPA outperformed other wavelengths selection methods considered in this study. The obtained results indicated the feasibility of FPA to improve the predictability of the PLSR and reduce the model complexity. The nonlinear regression models of support vector machine regression and Gaussian process regression (GPR) were further utilized to evaluate the superiority of using the FPA in the wavelength selection. The results demonstrated that utilizing the wavelength selection method of FPA and nonlinear regression model of GPR could improve the predictive performance.
Collapse
Affiliation(s)
- Pauline Ong
- Department of Biomechatronics Engineering, National Taiwan University, Taipei, Taiwan; Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia, Johor, Malaysia.
| | - Suming Chen
- Department of Biomechatronics Engineering, National Taiwan University, Taipei, Taiwan
| | - Chao-Yin Tsai
- Department of Biomechatronics Engineering, National Taiwan University, Taipei, Taiwan
| | - Yung-Kun Chuang
- Master Program in Food Safety, College of Nutrition, Taipei Medical University, Taipei, Taiwan; School of Food Safety, College of Nutrition, Taipei Medical University, Taipei, Taiwan; Nutrition Research Center, Taipei Medical University Hospital, Taipei, Taiwan
| |
Collapse
|
15
|
Hinojosa-Nogueira D, Pérez-Burillo S, Pastoriza de la Cueva S, Rufián-Henares JÁ. Green and white teas as health-promoting foods. Food Funct 2021; 12:3799-3819. [PMID: 33977999 DOI: 10.1039/d1fo00261a] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Tea is one of the most consumed beverages around the world and as such, it is constantly the object of novel research. This review focuses on the research performed during the last five years to provide an updated view of the current position of tea regarding human health. According to most authors, tea health benefits can be traced back to its bioactive components, mostly phenolic compounds. Among them, catechins are the most abundant. Tea has an important antioxidant capacity and anti-inflammatory properties, which make this beverage (or its extracts) a potential aid in the fight against several chronic diseases. On the other hand, some studies report the possibility of toxic effects and it is advisable to reduce tea consumption, such as in the last trimester of pregnancy. Additionally, new technologies are increasing researchers' possibilities to study the effect of tea on human gut microbiota and even against SARS CoV-2. This beverage favours some beneficial gut microbes, which could have important repercussions due to the influence of gut microbiota on human health.
Collapse
Affiliation(s)
- Daniel Hinojosa-Nogueira
- Departamento de Nutrición y Bromatología, Instituo de Nutrición Y Tecnología de los Alimentos, Centro de Investigación BIomédica, Universidad de Granada, Granada, Spain.
| | - Sergio Pérez-Burillo
- Departamento de Nutrición y Bromatología, Instituo de Nutrición Y Tecnología de los Alimentos, Centro de Investigación BIomédica, Universidad de Granada, Granada, Spain. and Department of Biochemistry and Molecular Biology, Boonshoft School of Medicine, Wright State University, Dayton, OH, USA
| | - Silvia Pastoriza de la Cueva
- Departamento de Nutrición y Bromatología, Instituo de Nutrición Y Tecnología de los Alimentos, Centro de Investigación BIomédica, Universidad de Granada, Granada, Spain.
| | - José Ángel Rufián-Henares
- Departamento de Nutrición y Bromatología, Instituo de Nutrición Y Tecnología de los Alimentos, Centro de Investigación BIomédica, Universidad de Granada, Granada, Spain. and Instituto de Investigación Biosanitaria ibs.GRANADA, Universidad de Granada, Granada, Spain
| |
Collapse
|
16
|
Ren G, Liu Y, Ning J, Zhang Z. Assessing black tea quality based on visible–near infrared spectra and kernel-based methods. J Food Compost Anal 2021. [DOI: 10.1016/j.jfca.2021.103810] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
|
17
|
Chen Y, Bin J, Zou C, Ding M. Discrimination of Fresh Tobacco Leaves with Different Maturity Levels by Near-Infrared (NIR) Spectroscopy and Deep Learning. JOURNAL OF ANALYTICAL METHODS IN CHEMISTRY 2021; 2021:9912589. [PMID: 34211798 PMCID: PMC8205606 DOI: 10.1155/2021/9912589] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Revised: 05/08/2021] [Accepted: 05/31/2021] [Indexed: 05/21/2023]
Abstract
The maturity affects the yield, quality, and economic value of tobacco leaves. Leaf maturity level discrimination is an important step in manual harvesting. However, the maturity judgment of fresh tobacco leaves by grower visual evaluation is subjective, which may lead to quality loss and low prices. Therefore, an objective and reliable discriminant technique for tobacco leaf maturity level based on near-infrared (NIR) spectroscopy combined with a deep learning approach of convolutional neural networks (CNNs) is proposed in this study. To assess the performance of the proposed maturity discriminant model, four conventional multiclass classification approaches-K-nearest neighbor (KNN), backpropagation neural network (BPNN), support vector machine (SVM), and extreme learning machine (ELM)-were employed for a comparative analysis of three categories (upper, middle, and lower position) of tobacco leaves. Experimental results showed that the CNN discriminant models were able to precisely classify the maturity level of tobacco leaves for the above three data sets with accuracies of 96.18%, 95.2%, and 97.31%, respectively. Moreover, the CNN models with strong feature extraction and learning ability were superior to the KNN, BPNN, SVM, and ELM models. Thus, NIR spectroscopy combined with CNN is a promising alternative to overcome the limitations of sensory assessment for tobacco leaf maturity level recognition. The development of a maturity-distinguishing model can provide an accurate, reliable, and scientific auxiliary means for tobacco leaf harvesting.
Collapse
Affiliation(s)
- Yi Chen
- Yunnan Academy of Tobacco Agricultural Sciences, Kunming, China
| | - Jun Bin
- College of Tobacco Science, Guizhou University, Guiyang, China
| | - Congming Zou
- Yunnan Academy of Tobacco Agricultural Sciences, Kunming, China
| | - Mengjiao Ding
- College of Tobacco Science, Guizhou University, Guiyang, China
| |
Collapse
|
18
|
Lin X, Sun DW. Recent developments in vibrational spectroscopic techniques for tea quality and safety analyses. Trends Food Sci Technol 2020. [DOI: 10.1016/j.tifs.2020.06.009] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|